Job Description
We are seeking a visionary Senior Machine Learning Engineer to lead the next generation of artificial intelligence solutions at Nexus Future Systems. In this pivotal role, you will architect scalable machine learning pipelines, optimize model performance for real-time inference, and collaborate with cross-functional teams to integrate AI capabilities into our core products. Join us in defining the future of intelligent automation and shaping the technological landscape of 2025 and beyond.
Why Join Us?
- Work with cutting-edge technologies including LLMs, Transformers, and Reinforcement Learning.
- Competitive compensation package and equity options.
- Flexible remote-first culture with offices in the heart of San Francisco.
- Opportunity to mentor junior engineers and drive technical strategy.
Key Responsibilities:
- Design, develop, and deploy end-to-end machine learning models and MLOps pipelines using Python, PyTorch, and TensorFlow.
- Collaborate with data scientists and software engineers to integrate ML models into production applications.
- Optimize existing models for speed, accuracy, and resource efficiency on cloud infrastructure (AWS/GCP).
- Implement robust monitoring, logging, and CI/CD practices to ensure model reliability and performance.
- Conduct rigorous A/B testing and experimental analysis to validate model improvements.
- Stay abreast of the latest research in deep learning and apply novel techniques to solve complex business problems.
Qualifications:
- Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field (3-5+ years of experience for non-PhD candidates).
- Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
- Proven experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Deep understanding of MLOps tools and methodologies (MLflow, Kubeflow, Airflow).
- Experience with data visualization libraries (Matplotlib, Seaborn) and big data processing tools (Spark, Hadoop).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
Responsibilities
- Design, develop, and deploy end-to-end machine learning models and MLOps pipelines using Python, PyTorch, and TensorFlow.
- Collaborate with data scientists and software engineers to integrate ML models into production applications.
- Optimize existing models for speed, accuracy, and resource efficiency on cloud infrastructure (AWS/GCP).
- Implement robust monitoring, logging, and CI/CD practices to ensure model reliability and performance.
- Conduct rigorous A/B testing and experimental analysis to validate model improvements.
- Stay abreast of the latest research in deep learning and apply novel techniques to solve complex business problems.
Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field (3-5+ years of experience for non-PhD candidates).
- Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
- Proven experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Deep understanding of MLOps tools and methodologies (MLflow, Kubeflow, Airflow).
- Experience with data visualization libraries (Matplotlib, Seaborn) and big data processing tools (Spark, Hadoop).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.